Hi Rich,
You can use the P/M/A calls to filter regardless of what
pre-processing algorithm you use. It is a completely separate
algorithm from MAS5, and I routinely use the P/M/A calls from the
mas5calls() function in the affy package in conjunction with gcrma values.
Cheers,
Jenny
At 01:14 PM 3/2/2011, Richard Friedman wrote:
>Dear Gordon,
>> Can you suggest how to define "some modest evidence of expression"
>in Affymetrix arrays filtered with RMA
>or GCRMA which does not give a presence-or-absence call?
>>Thanks and best wishes,
>Rich
>>On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote:
>>>Deaer Avhena,
>>>>I agree with Wolfgang that filtering is useful. In my lab, the
>>standard practice is to filter probes that fail to show some modest
>>evidence for expression on at least n arrays, where n is the minimum
>>group size. For example, if we compare wt (with 2 replicate arrays)
>>to a mutant (with 3 replicate arrays), we filter probes that are
>>Present on fewer than 2 arrays.
>>>>This is because we want to keep any probe that is expressed in at
>>least one of the experimental conditions. If a probe is expressed
>>in one of the conditions, then it should appear consistently across
>>the replicates for that condition.
>>>>Best wishes
>>Gordon
>>>>>Date: Mon, 28 Feb 2011 11:35:42 -0500
>>>From: avehna <avhena at gmail.com>
>>>To: whuber at embl.de>>>Cc: bioconductor at r-project.org>>>Subject: Re: [BioC] Detection calls and LIMMA
>>>>>>Dear Wolfgang,
>>>>>>Thank you for your response, I agree with you. I will read the
>>>paper now...
>>>>>>Best Regards,
>>>Avhena
>>>>>>On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at embl.de>
>>>wrote:
>>>>>>>Hi Avhena
>>>>>>>>it is not required, but properly applied filtering can increase
>>>>detection
>>>>power in your experiment while still controlling type-I error (false
>>>>positives). The example you mention seems to be one that you want
>>>>to keep
>>>>though, since it is a good candidate for being up-regulated in the
>>>>Treatment
>>>>condition. One possibly reasonable criterion would be, e.g., to
>>>>filter out
>>>>all probesets that are called 'Absent' on all arrays. Some further
>>>>discussion on the topic is also here:
>>>>>>>>[1] Bourgon, Gentleman and Huber. Independent filtering increases
>>>>detection
>>>>power for high-throughput experiments. PNAS, 107(21):9546-9551,
>>>>>>>> Best wishes
>>>> Wolfgang
>>>>>>>>>>>>Il Feb/28/11 6:51 AM, avehna ha scritto:
>>>>>>>>>Hi All,
>>>>>>>>>>I have a basic question. Is it required to filter the microarray
>>>>>data
>>>>>based
>>>>>on the detection calls (A/M/P) before analyzing it with LIMMA?
>>>>>>>>>>What if I have the following scenario (for example):
>>>>>>>>>> Control Control Control Treatment
>>>>>Treatment Treatment
>>>>>1367813_at A A P
>>>>>P P P
>>>>>>>>>>Please note that this gene is just "present/detected" once in the
>>>>>Control,
>>>>>but it is present in all the replicates of the treatment. In this
>>>>>case:
>>>>>what
>>>>>would be the right thing to do? To eliminate it from the analysis
>>>>>or keep
>>>>>it
>>>>>and consider it up or down depending on the signal of the
>>>>>treatment?
>>>>>>>>>>Thank a lot!
>>>>>Avhena
>>>>______________________________________________________________________
>>The information in this email is confidential and intend...{{dropped: 4}}
>>>>_______________________________________________
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